| Depression is a common mental illness.Its incidence has shown a trend of increasing year after year and has gradually become one of the major killers of human health.Therefore,how to effectively detect and treat depression is an urgent problem in medical research.Acupuncture is an emerging treatment of depression in recent years with the advantages of small side effects,low treatment cost,no drug-dependence effect,and other studies have shown that it can achieve the equivalent effect of drug therapy.Therefore,there is increasing recognition of the importance of acupuncture treatment of depression.At present,clinically,whether it is for the detection of depression or the evaluation of the effect of acupuncture treatment,most of the time some qualitative psychological scales are used.However,the examination through the scale is not only time consuming and inconvenient,but also the assessment results are easily affected by the patients' mental state at that time.In addition,the manifestations of depression are varied among different people,and the pathological causes are complex and changeable,it is easy to lead to a deviation from the patient's condition in the doctors' judgment.Therefore,from the perspective of being simple,practical and easy to be promoted in clinical practice,this article takes the "task state" heart rate variability(HRV)index of patients with depression as the research object,and explores quantitative indicators that can be used to detect depression and evaluate the effect of acupuncture treatment.Method mentioned in the paper can provide more help for doctors to diagnose the condition of depressed patients and assess the effect of treatment.The main contents of this paper are as follows:(1)Construction of depression detection model based on "task state" HRV parameters.Firstly,thirty-three “task state” heart rate variability parameters were calculated for depressed patients and the healthy control subjects in study.Next,characteristic parameters used to detect depression were extracted from the data through t-test and comparative analysis.The exhaustion method and principal component analysis algorithm(PCA),combined with support vector machine(SVM),were used to construct depression detection model.The area under the receiver operating characteristic curve(ROC)-AUC was used to determine the optimal classification model which was proved to be the one combined main feature extraction by PCA and SVM classifier.The experimental results showed that the depression detection accuracy of the model is 89.66%.(2)Study on the evaluation method for curative effect of acupuncture and moxibustion treatment of depression.Firstly,the 33 "task state" HRV parameters for those patients who received acupuncture and moxibustion treatment were calculated at the four therapeutic efficacy observation points.Next,algorithm was designed to analyze the changes of 33 HRV parameters at the four therapeutic efficacy observation points,and four quantitative indicators with therapeutic evaluation value were found out.The results of the study showed that among all "task state" HRV parameters,Standard deviation of RR interval(SDNN),the root mean square of the difference between adjacent RR intervals(RMSSD),and the Poincaré plot analysis parameters(SD1,SD2)could reveal the curative effect of acupuncture and moxibustion treatment of depression.The validity of the four quantitative parameters was further verified by the correlation analysis between the evaluating result of the four "task state" HRV parameters and psychometric scale test results and the t-test analysis between the corresponding indicators of healthy individuals and depressed patients.Finally,the physiological significance represented by the four quantitative parameters was discussed in conjunction with the related literature.This study provide new ideas and new methods for evaluating the efficacy of acupuncture and moxibustion in the treatment of depressive disorder. |
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